Structural information aware deep semi-supervised recurrent neural network for sentiment analysis

With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to understand the polarity beneath these comments. Currently a lot of approaches from natural language processing’s perspective have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analysis methods. In this research, we further investigate the structural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is playing important role in identifying the overall statement’s polarity. As a result a novel sentiment analysis model is proposed based on recurrent neural network, which takes the partial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.